
"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase        =				{
    """configuration_deberta""": ["""DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DebertaConfig""", """DebertaOnnxConfig"""],
    """tokenization_deberta""": ["""DebertaTokenizer"""],
}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase        =				["""DebertaTokenizerFast"""]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase        =				[
					    """DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """DebertaForMaskedLM""",
					    """DebertaForQuestionAnswering""",
					    """DebertaForSequenceClassification""",
					    """DebertaForTokenClassification""",
					    """DebertaModel""",
					    """DebertaPreTrainedModel""",
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase        =				[
					    """TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """TFDebertaForMaskedLM""",
					    """TFDebertaForQuestionAnswering""",
					    """TFDebertaForSequenceClassification""",
					    """TFDebertaForTokenClassification""",
					    """TFDebertaModel""",
					    """TFDebertaPreTrainedModel""",
					]


if TYPE_CHECKING:
					from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaOnnxConfig
					from .tokenization_deberta import DebertaTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_deberta_fast import DebertaTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_deberta import (
										    DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
										    DebertaForMaskedLM,
										    DebertaForQuestionAnswering,
										    DebertaForSequenceClassification,
										    DebertaForTokenClassification,
										    DebertaModel,
										    DebertaPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_deberta import (
										    TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFDebertaForMaskedLM,
										    TFDebertaForQuestionAnswering,
										    TFDebertaForSequenceClassification,
										    TFDebertaForTokenClassification,
										    TFDebertaModel,
										    TFDebertaPreTrainedModel,
										)


else:
					import sys

					UpperCAmelCase        =				_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




"""simple docstring"""






from collections import namedtuple

UpperCAmelCase        =				namedtuple("""from_to""", """from_ to""")

UpperCAmelCase        =				{
    """cubicmeter""": from_to(1, 1),
    """litre""": from_to(0.001, 1_0_0_0),
    """kilolitre""": from_to(1, 1),
    """gallon""": from_to(0.00_454, 264.172),
    """cubicyard""": from_to(0.76_455, 1.30_795),
    """cubicfoot""": from_to(0.028, 35.3_147),
    """cup""": from_to(0.000_236_588, 4_226.75),
}




def __magic_name__	(				_lowerCamelCase:		float,					_lowerCamelCase:		str,					_lowerCamelCase:		str			)					->						float:

	'''simple docstring'''




	if from_type not in METRIC_CONVERSION:
		raise ValueError(
		    F"""Invalid 'from_type' value: {from_type!r}  Supported values are:\n"""
		    + ''', '''.join(_lowerCamelCase			)			)
	if to_type not in METRIC_CONVERSION:
		raise ValueError(
		    F"""Invalid 'to_type' value: {to_type!r}.  Supported values are:\n"""
		    + ''', '''.join(_lowerCamelCase			)			)
	return value * METRIC_CONVERSION[from_type].from_ * METRIC_CONVERSION[to_type].to


if __name__ == "__main__":
					import doctest

					doctest.testmod()


